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Record W2916013158 · doi:10.1139/cgj-2018-0267

Estimating in situ relative density and friction angle of nearshore sand from portable free-fall penetrometer tests

2019· article· en· W2916013158 on OpenAlexvenueno aff
Ali Albatal, Nina Stark, Bernardo A. Castellanos

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsPenetrometerRelative densityGeotechnical engineeringFriction angleRange (aeronautics)GeologyMaterials scienceSoil scienceComposite material

Abstract

fetched live from OpenAlex

The friction angle of sand in the nearshore zone of Cannon Beach, Yakutat, Alaska, was estimated from the deceleration measured by a portable free-fall penetrometer (PFFP) at 72 test locations. A correlation between the relative density and PFFP’s maximum deceleration was developed from controlled PFFP deployments into sand of different relative densities. Two approaches were tested: (i) a correlation between relative density and friction angle and (ii) bearing capacity theory. For the former, laboratory vacuum triaxial tests were performed to adjust an existing correlation between relative density and friction angle for the tested nearshore sediments. In situ peak friction angles were then determined using this adjusted correlation and estimates of relative densities. The resulting in situ relative density and friction angle varied between 32%–88% and 44°–56°, respectively. Two bearing capacity–based methods suitable for shallow penetrations were tested. For this approach, equivalents of static cone resistance were determined from the measured decelerations considering the strain rate effect. A range of empirical strain rate coefficients K = 0.1–1.5 were tested. A K value between 0.2 and 0.4 yielded matching results between the two approaches. The estimated friction angles agreed well with expected values and may be applied to problems of sediment transport or early site assessment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.182
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations42
Published2019
Admission routes1
Has abstractyes

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